中文    English

Journal of Library and Information Science in Agriculture

   

Building Consumption Data Systems driven by AI Plus Expert for Scientific and Technical Literature Information Resources

Guanghui YE, Kai TU, Lina HU, Li HAN, Zhiming FENG   

  1. School of information management, Central China Normal University, Wuhan 430079
  • Received:2024-07-01 Online:2024-11-26

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Abstract:

[Purpose/Significance] Limited by the constraints of traditional literature classification systems, scientific and technical literature information resources face problems such as inadequate disclosure and resource utilization. At the same time, high-quality user-generated data cannot yet be integrated as data elements into services related to scientific and technical literature services, which prevents these services from adapting to the context of the open science and meeting the diverse knowledge needs of readers. This study aims to harness the technological breakthrough potential of AI to build a consumer-end data system for scientific and technical literature information resources driven by AI and experts. This will help to overcome the shortcomings of traditional services, such as the lack of supporting reading information and low interactivity between users, with the hope of promoting the optimization process of scientific and technical literature information resource services. [Method/Process] First, the study analyzes the four-dimensional value representation of the consumer-end data systems for scientific and technical literature information resources, including the intrinsic value, the tool value, the academic value, and the future value of annotation data. Then, following the processing flow of consumer-end data, namely the collection phase, utilization phase, and management phase, the paper proposes principles for the construction of consumer-end data systems. Furthermore, the paper deconstructs and analyzes the risks associated with the involvement of AI in the construction of consumer-end data systems, including four types of risks: machine algorithm risks, annotation content risks, annotation data risks and application risks. Finally, based on the degree of AI involvement in data annotation work, three innovative models of AI plus expert collaborates with user to accomplish data annotation for scientific and technical literature information resources are designed: the AI plus expert-assisted data annotation model, the AI plus expert collaborative data annotation model, and the AI plus expert-led data annotation model. [Results/Conclusions] Under the AI plus expert-assisted data annotation model, AI acts as a tool to complete surface-level information processing based on rules set by experts to assist users in data annotation. In the AI plus expert collaborative data annotation model, AI completes the review of pre-annotation tags for scientific and technical literature information resources, transforming users from a self-generated tag mode to an AI-generated data tag evaluation and selection mode, with experts assisting in the final review of data tag quality. In the AI plus expert-led data annotation model, users provide data annotation requirements, experts guide the process, and data annotation is automatically completed by the AI4S platform.

Key words: scientific and technical literature information resources, AI, system construction, data annotation, pattern design

CLC Number: 

  • G251

Table 1

Guidelines for building the system based on the process of consumer data processing"

消费端数据处理过程 建设原则 含义
消费端数据收集阶段 分类分级 对异类数据进行分级分类管理
新颖性 收集的数据需要具有新颖性，避免观点陈旧
价值性 收集的数据需要具有价值性
合规性 收集的数据不能含有歧视性
结构化 收集的数据需要结构化良好
消费端数据利用阶段 多维性 多维度对标注数据进行利用
准确性 提供的标注数据需要能与用户需求匹配
共享性 支持消费端数据共建共享
消费端数据管理阶段 可改善性 根据用户的反馈，能够对数据进行内容改善
技术保障性 需要建立技术平台，提供数据周密管理保障

Fig.1

Ethical risks of artificial intelligence in the field of data annotation"

Fig.2

Innovative model for data annotation of scientific and technical literature information resources with AI and expert collaboration"

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